Title :
Resolving Superimposed MUAPs Using Particle Swarm Optimization
Author :
Marateb, Hamid Reza ; McGill, Kevin C.
Author_Institution :
Dipt. di Elettron., Politec. di Torino, Turin
fDate :
3/1/2009 12:00:00 AM
Abstract :
This paper presents an algorithm to resolve superimposed action potentials encountered during the decomposition of electromyographic signals. The algorithm uses particle swarm optimization with a variety of features including randomization, crossover, and multiple swarms. In a simulation study involving realistic superpositions of two to five motor-unit action potentials, the algorithm had an accuracy of 98%.
Keywords :
electromyography; medical signal processing; particle swarm optimisation; crossover; electromyographic signal decomposition; motor-unit action potentials; multiple swarms; particle swarm optimization; randomization; superimposed MUAP; superimposed action potentials; Discrete Fourier transforms; Electromyography; Genetic algorithms; Interference; Interpolation; Particle swarm optimization; Research and development; Signal resolution; Space exploration; Timing; Alignment; decomposition; electromyography; particle swarm optimization; superposition; Action Potentials; Algorithms; Computer Simulation; Electromyography; Humans; Models, Neurological; Motor Neurons; Sensitivity and Specificity; Signal Processing, Computer-Assisted;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2008.2005953
